Haralick, R.M.[Robert M.], and
Zhuang, X.[Xinhua],
A Note on 'Rigid Body Motion from Depth and Optical Flow,',
CVGIP(34), No. 3, June 1986, pp. 372-387.
A comment on (
See also Rigid Body Motion from Depth and Optic Flow. ) that computed the motion parameters.
The claim is that there is a theoretical solution given
4 uncoplanar points, with optical flow and depth.
BibRef
8606
Zhuang, X.,
Haralick, R.M., and
Zhao, Y.,
From Depth and Optical Flow to Rigid Body Motion,
CVPR88(393-397).
IEEE Abstract. IEEE Top Reference. Builds on the other papers by the authors.
BibRef
8800
Haralick, R.M.[Robert M.], and
Lee, J.S.[Jong Soo],
The Facet Approach to Optic Flow,
DARPA83(84-93).
Facet approach: filter the local neighborhood and interpret the processing in
terms of how it affects the best fit function (from the filtering). By
assuming gray tone partial in row, column and time being equal for the
corresponding point you can find the matching point (also intensity is the
same). (Of course you can.)
BibRef
8300
Zhuang, X.[Xinhua],
Huang, T.S.,
Ahuja, N., and
Haralick, R.M.[Robert M.],
A Simplified Linear Optical Flow-Motion Algorithm,
CVGIP(42), No. 3, June 1988, pp. 334-344.
BibRef
8806
Earlier:
Rigid Body Motion and the Optic Flow Image,
CAIA84(366-375).
Given the optical flow, establish the rigid body motion with only
instantaneous rotation and translation.
BibRef
Ballard, D.H.[Dana H.], and
Kimball, O.A.,
Rigid Body Motion from Depth and Optic Flow,
CVGIP(22), No. 1, April 1983, pp. 95-115.
WWW Version.
Hough.
Hough technique to determine body motion. See the Haralick comment
above. This uses 3-D optical flow and acceleration of points.
The 5-D Hough space is generated.
BibRef
8304
Scheuing, A.,
Niemann, H.,
Computing Depth from Stereo Images by Using Optical Flow,
PRL(4), 1986, pp. 205-212.
BibRef
8600
Tistarelli, M., and
Vernon, D.,
Using Camera Motion to Estimate
Range for Robotic Parts Manipulation,
RA(6), No. 5, December 1990, pp. 509-521.
Depth from Motion.
Active Vision. Estimate depth and structure from controlled motion
of a robot arm.
BibRef
9012
Grosso, E.,
Tistarelli, M.,
Active Dynamic Stereo Vision,
PAMI(17), No. 11, November 1995, pp. 1117-1128.
IEEE Top Reference. Originally printed as:
BibRef
9511
PAMI(17), No. 9, September 1995, pp. 868-879.
IEEE Abstract. IEEE Top Reference.
WWW Version. But there were errors in the printing.
Motion and stereo to determine structure and free space.
See also Dynamic Aspects in Active Vision.
BibRef
Grosso, E.,
Sandini, G., and
Tistarelli, M.,
3-D Object Reconstruction Using Stereo and Motion,
SMC(19), No. 6, Nov/Dec 1989, pp. 1465-1476.
Shape from Motion.
3D Reconstruction. Control the motion and extract depth from both stereo and motion.
This gives more constraints on the final results.
BibRef
8900
Grosso, E.,
Tistarelli, M., and
Sandini, G.,
Active/Dynamic Stereo for Navigation,
ECCV92(516-525).
WWW Version.
Dynamic Stereo.
BibRef
9200
Tistarelli, M.,
Grosso, E., and
Sandini, G.,
Dynamic Stereo in Visual Navigation,
CVPR91(186-193).
IEEE Abstract. IEEE Top Reference. Depth from stereo and Optical flow.
BibRef
9100
Grosso, E., and
Tistarelli, M.,
Active/Dynamic Stereo: A General Framework,
CVPR93(732-734).
IEEE Abstract. IEEE Top Reference.
BibRef
9300
Sandini, G., and
Tistarelli, M.,
Active Tracking Strategy for Monocular Depth Inference over
Multiple Frames,
PAMI(12), No. 1, January 1990, pp. 13-27.
IEEE Abstract. IEEE Top Reference.
WWW Version. Depth is inferred from motion of edge contours over time (axial motion).
Uses two sampling rates, one to get instantaneous optical flow, the other
to derive depth.
BibRef
9001
Akgul, Y.S.[Yusuf Sinan],
Kambhamettu, C.[Chandra],
A coarse-to-fine deformable contour optimization framework,
PAMI(25), No. 2, February 2003, pp. 174-186.
IEEE Abstract. IEEE Top Reference.
WWW Version.
0301
BibRef
Earlier:
A New Multi-Level Framework for Deformable Contour Optimization,
CVPR99(II: 465-470).
IEEE Abstract. IEEE Top Reference.
WWW Version. Used in:
See also Extracting Nonrigid Motion and 3D Structure of Hurricanes from Satellite Image Sequences without Correspondences.
BibRef
Akgul, Y.S.[Yusuf Sinan],
Kambhamettu, C.[Chandra],
A Scale-Space Approach for Deformable Contour Optimization,
ScaleSpace99(410-422).
BibRef
9900
Palaniappan, K.,
Kambhamettu, C.[Chandra],
Hasler, F.[Frederick],
Goldgof, D.[Dmitry],
Structure and Semi-Fluid Motion Analysis of
Stereoscopic Satellite Images for Cloud Tracking,
ICCV95(659-665).
WWW Version.
WWW Version.
BibRef
9500
Khamene, A.[Ali],
Negahdaripour, S.[Shahriar],
Motion and structure from multiple cues; image motion, shading flow,
and stereo disparity,
CVIU(90), No. 1, April 2003, pp. 99-127.
WWW Version.
0306
BibRef
Zhang, Y.[Ye],
Kambhamettu, C.[Chandra],
On 3-D scene flow and structure recovery from multiview image sequences,
SMC-B(33), No. 4, August 2003, pp. 592-606.
IEEE Abstract. IEEE Top Reference.
0308
BibRef
Earlier:
On 3D Scene Flow and Structure Estimation,
CVPR01(II:778-785).
IEEE Abstract. IEEE Top Reference.
0110
BibRef
Zhang, Y.[Ye],
Kambhamettu, C.[Chandra],
Integrated 3D Scene Flow and Structure Recovery from Multiview Image
Sequences,
CVPR00(II: 674-681).
IEEE Abstract. IEEE Top Reference.
WWW Version.
0005
BibRef
Demirdjian, D.,
Darrell, T.J.,
Using Multiple-Hypothesis Disparity Maps and Image Velocity for 3-D
Motion Estimation,
IJCV(47), No. 1-3, April-June 2002, pp. 219-228.
WWW Version.
0203
BibRef
Earlier:
SMBV01(xx-yy).
0110
BibRef
Yoda, I.[Ikushi], and
Sakaue, K.[Katsuhiko],
Utilization of Stereo Disparity and Optical Flow Information
for Human Interaction,
ICCV98(1109-1114).
WWW Version.
BibRef
9800
Sudhir, G.,
Banerjee, S.,
Biswas, K.K.,
Bahl, R.,
A Cooperative Integration of Stereopsis and Optic Flow Computation,
ICPR94(A:356-360).
WWW Version.
BibRef
9400
Moezzi, S.,
Bartlett, S.L., and
Weymouth, T.E.,
The Camera Stability Problem and Dynamic Stereo Vision,
CVPR91(109-114).
IEEE Abstract. IEEE Top Reference. Match depth maps.
BibRef
9100
Weymouth, T.E.,
Moezzi, S.,
Wide Base-Line Dynamic Stereo: Approximation and Refinement,
CVPR88(183-188).
IEEE Abstract. IEEE Top Reference.
BibRef
8800
Chapter on Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Spatio-Temporal Analysis -- Many Frames .